The Ex-DeepMind Researcher Taking On China’s Open Source AI

Jason Calacanis (0:00)

All right, everybody, next on the program here at This Week in Startups, I am super excited to have one of the few leaders here in the United States for open source models, which is something we desperately need, as you've heard over and over again here on This Week in Startups, This Week in AI, on the All In Podcast. We are very much in the lead when it comes to frontier models, but frontier models have many shortcomings, have many Achilles' heels. One of them is that you can be rug pulled at any time, and the frontier model could take your model away from you. In other words, they're not reliable. Another is the frontier models are viewed increasingly as competition. Number three, you are renting your intelligence, you don't own it, you don't have sovereignty over your knowledge, over your business. There are a number of important issues that founders have to make when picking between open source and frontier models. And so we're going to talk about all that today with Misha Laskin. He is the co-founder and CEO of Reflection AI. That's a lab here in the United States, an American company that is building open weight AI models that companies, governments, organizations can run for themselves. Misha was formerly at Google's DeepMind or Gemini, working on Gemini, I guess.

You know, the branding over there is a little bit confusing. But welcome to the program, Misha. How are you doing, Misha?

Misha Laskin (1:28)

I'm doing really well, Jason. Thanks for having me.

Jason Calacanis (1:30)

So you heard my little preamble, which is now kind of becoming codified, I think, in the past three to six months in almost everybody's mind who is applying superintelligence, trademark President Donald J. Trump. So when we look, have you started using superintelligence yet? Have you have you stopped using AI?

Misha Laskin (1:54)

Yeah, we started using superintelligence years ago. Well, a few years ago.

I mean, that was kind of the the when I got into AI and saw AlphaGo happen, it was that was the first semblance of superintelligence. And it was pretty we thought it was a general technology. And so it was something codified some years ago. But I think now you're actually starting to see a general superintelligence and that's has some pretty remarkable consequences.

Jason Calacanis (2:22)

Yeah. So let's start with you've heard this cadre of issues start to become codified into almost a platform that people working on AI every day, people working with superintelligence every day are now in general agreement, dare I say alignment on.

So you've been working on open source for a while. I think in some ways the world has caught up. What is the reason why corporations, startups, smaller companies, and even the sovereign individual, why should they care about open source language models and open weight language models? Why should they care?

Misha Laskin (3:06)

It's a really good question, Jason, and I actually think there's something that you said earlier that is on the point, which is this notion of a rental versus ownership market for intelligence. We can think about the analogy to real estate. In real estate, you have both rental and ownership. And typically, the progression of a consumer is that you start off in the rental market. And then as you grow up, you get a family, you have kind of more financial maturity. Ownership starts making sense for a number of reasons, even if it's a pain to own a house. You have to fix the pipes when they're broken and so forth, but you own it and there are benefits to it.

In AI, the commercial market for AI has only existed for the last few years, three, four years since Chad GPT came out. And the initial market for AI was entirely a rental market. As early adopters come in and are testing things out, trying out workloads, you haven't really scaled to production in a big way.

That's when a rental market makes sense. But by the time you've scaled something to production, and this is your basically almost core asset, core IP, you're generating $100 million or spending $100 million on it, ownership starts making sense for a number of reasons around control of your data, lowering your costs, being able to customize the model against your data, being able to optimize your margins. All these things start making sense when you're at scale. What we've seen is that the AI market has matured very quickly, exponentially. And that's why six months ago, no one was really talking about open source. And now it's the opposite, right? Everyone's talking about open source, and it's because the market has matured exponentially, and a lot of buyers are looking to own their intelligence now.